Overview

Dataset statistics

Number of variables12
Number of observations129978
Missing cells0
Missing cells (%)0.0%
Duplicate rows231
Duplicate rows (%)0.2%
Total size in memory11.9 MiB
Average record size in memory96.0 B

Variable types

Numeric11
Categorical1

Alerts

Dataset has 231 (0.2%) duplicate rowsDuplicates
dropped_frames_mean is highly correlated with dropped_frames_maxHigh correlation
dropped_frames_max is highly correlated with dropped_frames_meanHigh correlation
bitrate_mean is highly correlated with yHigh correlation
y is highly correlated with bitrate_meanHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_maxHigh correlation
dropped_frames_max is highly correlated with dropped_frames_meanHigh correlation
bitrate_mean is highly correlated with yHigh correlation
y is highly correlated with bitrate_meanHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_maxHigh correlation
dropped_frames_max is highly correlated with dropped_frames_meanHigh correlation
rtt_mean is highly correlated with yHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_std and 2 other fieldsHigh correlation
dropped_frames_std is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
dropped_frames_max is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
bitrate_mean is highly correlated with bitrate_std and 1 other fieldsHigh correlation
bitrate_std is highly correlated with bitrate_meanHigh correlation
packet_loss_rate is highly correlated with dropped_frames_mean and 1 other fieldsHigh correlation
packet_loss_std is highly correlated with packet_loss_rateHigh correlation
y is highly correlated with rtt_mean and 1 other fieldsHigh correlation
fps_std has 19684 (15.1%) zeros Zeros
rtt_std has 3445 (2.7%) zeros Zeros
dropped_frames_std has 113278 (87.2%) zeros Zeros
bitrate_std has 1557 (1.2%) zeros Zeros
packet_loss_std has 74872 (57.6%) zeros Zeros

Reproduction

Analysis started2022-11-08 19:21:19.850959
Analysis finished2022-11-08 19:21:33.361009
Duration13.51 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

fps_mean
Real number (ℝ≥0)

Distinct49723
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5622421125
Minimum1.23455266 × 10-5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:33.410835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.23455266 × 10-5
5-th percentile0.2917847025
Q10.4775363961
median0.5507607132
Q30.6581307569
95-th percentile0.8571515301
Maximum1
Range0.9999876545
Interquartile range (IQR)0.1805943608

Descriptive statistics

Standard deviation0.1642507189
Coefficient of variation (CV)0.2921352123
Kurtosis0.7158894519
Mean0.5622421125
Median Absolute Deviation (MAD)0.08780190174
Skewness0.09041120381
Sum73079.1053
Variance0.02697829866
MonotonicityNot monotonic
2022-11-08T22:21:33.495645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11712
 
1.3%
0.50012496881382
 
1.1%
0.50024987511350
 
1.0%
0.66677774081024
 
0.8%
0.600079984861
 
0.7%
0.5556049328843
 
0.6%
0.5000833194715
 
0.6%
0.5714897872686
 
0.5%
0.5000624922623
 
0.5%
0.545495864481
 
0.4%
Other values (49713)120301
92.6%
ValueCountFrequency (%)
1.23455266 × 10-51
 
< 0.1%
1.282034846 × 10-51
 
< 0.1%
1.388869599 × 10-51
 
< 0.1%
1.449254359 × 10-52
 
< 0.1%
1.470566609 × 10-58
< 0.1%
1.562475586 × 10-53
 
< 0.1%
1.587276392 × 10-54
< 0.1%
1.612877212 × 10-54
< 0.1%
1.694886527 × 10-53
 
< 0.1%
1.724108205 × 10-54
< 0.1%
ValueCountFrequency (%)
11712
1.3%
0.99926901871
 
< 0.1%
0.99916459281
 
< 0.1%
0.99905305181
 
< 0.1%
0.99898424771
 
< 0.1%
0.99893895944
 
< 0.1%
0.99884262472
 
< 0.1%
0.99878738221
 
< 0.1%
0.998638288610
 
< 0.1%
0.9986308821
 
< 0.1%

fps_std
Real number (ℝ≥0)

ZEROS

Distinct68797
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08146167738
Minimum0
Maximum0.5102436748
Zeros19684
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:33.573244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02269595484
median0.06797855323
Q30.1247916282
95-th percentile0.2148966625
Maximum0.5102436748
Range0.5102436748
Interquartile range (IQR)0.1020956734

Descriptive statistics

Standard deviation0.07094999995
Coefficient of variation (CV)0.870961687
Kurtosis0.914718859
Mean0.08146167738
Median Absolute Deviation (MAD)0.04924863816
Skewness0.9858631627
Sum10588.2259
Variance0.005033902493
MonotonicityNot monotonic
2022-11-08T22:21:33.650794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019684
 
15.1%
0.04081666571400
 
0.3%
0.02041037349366
 
0.3%
0.1020110671341
 
0.3%
0.05101828174328
 
0.3%
0.0340150217313
 
0.2%
0.02915642697300
 
0.2%
0.06801870884269
 
0.2%
0.02551232911259
 
0.2%
0.01570064958218
 
0.2%
Other values (68787)107500
82.7%
ValueCountFrequency (%)
019684
15.1%
2.777152969 × 10-51
 
< 0.1%
0.00010323174511
 
< 0.1%
0.0001202276681
 
< 0.1%
0.00023163234863
 
< 0.1%
0.00023723503973
 
< 0.1%
0.00026030726511
 
< 0.1%
0.00032037722172
 
< 0.1%
0.00038335891511
 
< 0.1%
0.00041396207092
 
< 0.1%
ValueCountFrequency (%)
0.51024367481
< 0.1%
0.50846890882
< 0.1%
0.50658596691
< 0.1%
0.50310704812
< 0.1%
0.50310704811
< 0.1%
0.50031127171
< 0.1%
0.4959636481
< 0.1%
0.49404131371
< 0.1%
0.49321736081
< 0.1%
0.48914073521
< 0.1%

rtt_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77381
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2193421264
Minimum1.391981989 × 10-7
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:33.728525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.391981989 × 10-7
5-th percentile0.01988621357
Q10.08891946182
median0.1878190204
Q30.3152932073
95-th percentile0.5243054913
Maximum1
Range0.9999998608
Interquartile range (IQR)0.2263737455

Descriptive statistics

Standard deviation0.1625906117
Coefficient of variation (CV)0.7412648647
Kurtosis0.8900741795
Mean0.2193421264
Median Absolute Deviation (MAD)0.1093294552
Skewness0.9712756904
Sum28509.65091
Variance0.026435707
MonotonicityNot monotonic
2022-11-08T22:21:33.807665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1668055324146
 
0.1%
0.2501874531142
 
0.1%
0.0003332222592122
 
0.1%
0.271015579496
 
0.1%
0.347439742394
 
0.1%
0.260601516389
 
0.1%
0.430745307188
 
0.1%
0.375208263988
 
0.1%
0.281429642686
 
0.1%
0.2397733985
 
0.1%
Other values (77371)128942
99.2%
ValueCountFrequency (%)
1.391981989 × 10-73
 
< 0.1%
1.693766651 × 10-71
 
< 0.1%
1.983732993 × 10-72
 
< 0.1%
2.059731811 × 10-71
 
< 0.1%
2.242151964 × 10-71
 
< 0.1%
2.352940623 × 10-71
 
< 0.1%
2.378686399 × 10-74
 
< 0.1%
2.409057478 × 10-711
< 0.1%
2.818488495 × 10-79
< 0.1%
3.079764989 × 10-76
< 0.1%
ValueCountFrequency (%)
112
< 0.1%
0.98921574974
 
< 0.1%
0.98872555653
 
< 0.1%
0.98848045996
 
< 0.1%
0.98823536332
 
< 0.1%
0.98799026672
 
< 0.1%
0.986046576519
< 0.1%
0.97058852361
 
< 0.1%
0.96527795861
 
< 0.1%
0.96506094591
 
< 0.1%

rtt_std
Real number (ℝ≥0)

ZEROS

Distinct95702
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08500083073
Minimum0
Maximum0.5084689088
Zeros3445
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:33.887371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003480862324
Q10.02823232841
median0.07057364112
Q30.1286033413
95-th percentile0.2120806493
Maximum0.5084689088
Range0.5084689088
Interquartile range (IQR)0.1003710129

Descriptive statistics

Standard deviation0.0680208361
Coefficient of variation (CV)0.8002373097
Kurtosis0.4993914215
Mean0.08500083073
Median Absolute Deviation (MAD)0.04747528565
Skewness0.8957362395
Sum11048.23798
Variance0.004626834144
MonotonicityNot monotonic
2022-11-08T22:21:33.967649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03445
 
2.7%
0.0510182817482
 
0.1%
0.0705648216274
 
0.1%
0.154716968271
 
0.1%
0.0680187088470
 
0.1%
0.160460983867
 
0.1%
0.0844368813767
 
0.1%
0.116047393563
 
< 0.1%
0.117622468762
 
< 0.1%
0.11669507662
 
< 0.1%
Other values (95692)125915
96.9%
ValueCountFrequency (%)
03445
2.7%
0.00018573608691
 
< 0.1%
0.00020270500752
 
< 0.1%
0.00021171847531
 
< 0.1%
0.00024009895721
 
< 0.1%
0.00024228370811
 
< 0.1%
0.00024443909371
 
< 0.1%
0.00024772317662
 
< 0.1%
0.00026039208851
 
< 0.1%
0.00026831996721
 
< 0.1%
ValueCountFrequency (%)
0.50846890882
< 0.1%
0.50846890881
< 0.1%
0.50310704811
< 0.1%
0.49990726761
< 0.1%
0.49880120281
< 0.1%
0.49835165621
< 0.1%
0.49404131371
< 0.1%
0.49233214211
< 0.1%
0.48876875161
< 0.1%
0.48401650711
< 0.1%

dropped_frames_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9692
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3395165027
Minimum2.976181619 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.048325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.976181619 × 10-6
5-th percentile3.999840006 × 10-5
Q10.000999000999
median0.01818181818
Q31
95-th percentile1
Maximum1
Range0.9999970238
Interquartile range (IQR)0.999000999

Descriptive statistics

Standard deviation0.4603844533
Coefficient of variation (CV)1.356000223
Kurtosis-1.455718471
Mean0.3395165027
Median Absolute Deviation (MAD)0.0181342014
Skewness0.7230659926
Sum44129.67598
Variance0.2119538448
MonotonicityNot monotonic
2022-11-08T22:21:34.126559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142173
32.4%
0.0009990009995310
 
4.1%
0.00016663889351766
 
1.4%
7.691716022 × 10-51638
 
1.3%
0.0001999600081286
 
1.0%
7.142346975 × 10-51253
 
1.0%
0.00014283673761249
 
1.0%
8.332638947 × 10-51230
 
0.9%
0.00011109876681227
 
0.9%
0.00024993751561212
 
0.9%
Other values (9682)71634
55.1%
ValueCountFrequency (%)
2.976181619 × 10-624
 
< 0.1%
6.024060096 × 10-68
 
< 0.1%
6.097523796 × 10-618
 
< 0.1%
6.134931688 × 10-632
< 0.1%
7.092148283 × 10-66
 
< 0.1%
7.692248521 × 10-667
0.1%
8.403290729 × 10-610
 
< 0.1%
8.546935496 × 10-623
 
< 0.1%
8.62061534 × 10-630
< 0.1%
8.69557656 × 10-64
 
< 0.1%
ValueCountFrequency (%)
142173
32.4%
0.99275378071
 
< 0.1%
0.98397559161
 
< 0.1%
0.96084384531
 
< 0.1%
0.95833455881
 
< 0.1%
0.95312573243
 
< 0.1%
0.94339729441
 
< 0.1%
0.94230880185
 
< 0.1%
0.91310146681
 
< 0.1%
0.89070596891
 
< 0.1%

dropped_frames_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8970
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0153027999
Minimum0
Maximum0.5107428154
Zeros113278
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.203539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1801011323
Maximum0.5107428154
Range0.5107428154
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05717969185
Coefficient of variation (CV)3.736550974
Kurtosis18.62567572
Mean0.0153027999
Median Absolute Deviation (MAD)0
Skewness4.248593144
Sum1989.027325
Variance0.00326951716
MonotonicityNot monotonic
2022-11-08T22:21:34.280721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0113278
87.2%
0.20392022554
 
< 0.1%
0.0130148785752
 
< 0.1%
0.0110065017842
 
< 0.1%
0.0124606477642
 
< 0.1%
0.00935651806341
 
< 0.1%
0.0116725738139
 
< 0.1%
0.0104920416335
 
< 0.1%
0.0106734226734
 
< 0.1%
0.0125229453734
 
< 0.1%
Other values (8960)16327
 
12.6%
ValueCountFrequency (%)
0113278
87.2%
0.0012522876871
 
< 0.1%
0.0014067506181
 
< 0.1%
0.0017153145371
 
< 0.1%
0.0021618711252
 
< 0.1%
0.0024105179252
 
< 0.1%
0.0025838172331
 
< 0.1%
0.0028101563253
 
< 0.1%
0.0028120312771
 
< 0.1%
0.0028257690626
 
< 0.1%
ValueCountFrequency (%)
0.51074281541
< 0.1%
0.51074256861
< 0.1%
0.50989015581
< 0.1%
0.50884208861
< 0.1%
0.50860410061
< 0.1%
0.50362993731
< 0.1%
0.50272552491
< 0.1%
0.50267872111
< 0.1%
0.50267872111
< 0.1%
0.50195979721
< 0.1%

dropped_frames_max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3512
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3939412929
Minimum2.976181619 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.358664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.976181619 × 10-6
5-th percentile3.999840006 × 10-5
Q10.000999000999
median0.03710730914
Q31
95-th percentile1
Maximum1
Range0.9999970238
Interquartile range (IQR)0.999000999

Descriptive statistics

Standard deviation0.4710051326
Coefficient of variation (CV)1.19562265
Kurtosis-1.74658186
Mean0.3939412929
Median Absolute Deviation (MAD)0.03705731164
Skewness0.4677616733
Sum51203.70137
Variance0.221845835
MonotonicityNot monotonic
2022-11-08T22:21:34.434507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146016
35.4%
0.0009990009995310
 
4.1%
0.00016663889351766
 
1.4%
7.691716022 × 10-51638
 
1.3%
0.0001999600081286
 
1.0%
7.142346975 × 10-51253
 
1.0%
0.00014283673761249
 
1.0%
8.332638947 × 10-51230
 
0.9%
0.00011109876681227
 
0.9%
0.00024993751561212
 
0.9%
Other values (3502)67791
52.2%
ValueCountFrequency (%)
2.976181619 × 10-624
 
< 0.1%
6.024060096 × 10-68
 
< 0.1%
6.097523796 × 10-618
 
< 0.1%
6.134931688 × 10-632
< 0.1%
7.092148283 × 10-66
 
< 0.1%
7.692248521 × 10-667
0.1%
8.403290729 × 10-610
 
< 0.1%
8.546935496 × 10-623
 
< 0.1%
8.62061534 × 10-630
< 0.1%
8.69557656 × 10-64
 
< 0.1%
ValueCountFrequency (%)
146016
35.4%
0.99230775155
 
< 0.1%
0.98888901232
 
< 0.1%
0.98863649282
 
< 0.1%
0.98823543253
 
< 0.1%
0.98734193241
 
< 0.1%
0.98648666911
 
< 0.1%
0.9861113044
 
< 0.1%
0.98571448981
 
< 0.1%
0.98529433393
 
< 0.1%

bitrate_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct128047
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3972422314
Minimum1.596525934 × 10-8
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.513138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.596525934 × 10-8
5-th percentile0.04422010648
Q10.2676696599
median0.4502050349
Q30.5335234602
95-th percentile0.6219905501
Maximum1
Range0.999999984
Interquartile range (IQR)0.2658538003

Descriptive statistics

Standard deviation0.1813835187
Coefficient of variation (CV)0.4566068368
Kurtosis-0.5543713007
Mean0.3972422314
Median Absolute Deviation (MAD)0.1076966463
Skewness-0.5649295249
Sum51632.75075
Variance0.03289998086
MonotonicityNot monotonic
2022-11-08T22:21:34.588364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.591040164459
 
< 0.1%
0.575627946556
 
< 0.1%
0.478867483156
 
< 0.1%
0.49981914855
 
< 0.1%
0.51184600154
 
< 0.1%
0.97282054154
 
< 0.1%
0.593255275946
 
< 0.1%
0.563920202634
 
< 0.1%
0.54907570634
 
< 0.1%
0.293461106234
 
< 0.1%
Other values (128037)129496
99.6%
ValueCountFrequency (%)
1.596525934 × 10-83
< 0.1%
2.315833299 × 10-81
 
< 0.1%
2.516356253 × 10-83
< 0.1%
4.232804054 × 10-83
< 0.1%
4.458513333 × 10-84
< 0.1%
4.470072662 × 10-83
< 0.1%
4.594321208 × 10-82
< 0.1%
4.594321208 × 10-81
 
< 0.1%
5.029421866 × 10-84
< 0.1%
5.265374617 × 10-83
< 0.1%
ValueCountFrequency (%)
15
 
< 0.1%
0.99613505351
 
< 0.1%
0.97282054154
< 0.1%
0.94875293851
 
< 0.1%
0.94050234556
 
< 0.1%
0.93344136461
 
< 0.1%
0.93119077711
 
< 0.1%
0.92647244763
 
< 0.1%
0.92343363071
 
< 0.1%
0.92315586651
 
< 0.1%

bitrate_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct128135
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1127715903
Minimum0
Maximum0.4417078057
Zeros1557
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.665914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003140669589
Q10.06061048876
median0.1090680973
Q30.1606068496
95-th percentile0.2359203414
Maximum0.4417078057
Range0.4417078057
Interquartile range (IQR)0.09999636084

Descriptive statistics

Standard deviation0.07086043575
Coefficient of variation (CV)0.6283536088
Kurtosis-0.2400581777
Mean0.1127715903
Median Absolute Deviation (MAD)0.04995362614
Skewness0.3742006228
Sum14657.82577
Variance0.005021201354
MonotonicityNot monotonic
2022-11-08T22:21:34.738458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01557
 
1.2%
0.133731673512
 
< 0.1%
4.593243676 × 10-57
 
< 0.1%
0.036173784687
 
< 0.1%
0.10213035766
 
< 0.1%
0.0012701375036
 
< 0.1%
0.00045654391836
 
< 0.1%
0.10177512055
 
< 0.1%
0.00029245636635
 
< 0.1%
0.096687840175
 
< 0.1%
Other values (128125)128362
98.8%
ValueCountFrequency (%)
01557
1.2%
2.220304862 × 10-51
 
< 0.1%
2.537910231 × 10-51
 
< 0.1%
2.814839794 × 10-51
 
< 0.1%
3.048749498 × 10-51
 
< 0.1%
3.055513577 × 10-51
 
< 0.1%
3.274563835 × 10-51
 
< 0.1%
3.523634381 × 10-51
 
< 0.1%
3.576511471 × 10-51
 
< 0.1%
3.581499427 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.44170780571
< 0.1%
0.43638699491
< 0.1%
0.43239233181
< 0.1%
0.43125943851
< 0.1%
0.42522134221
< 0.1%
0.42149119531
< 0.1%
0.41990321661
< 0.1%
0.41861776831
< 0.1%
0.4182957891
< 0.1%
0.41536853191
< 0.1%

packet_loss_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18999
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1779903221
Minimum1.086944707 × 10-5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.814363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.086944707 × 10-5
5-th percentile0.0001428367376
Q10.000999000999
median0.03170539845
Q30.1066352635
95-th percentile1
Maximum1
Range0.9999891306
Interquartile range (IQR)0.1056362625

Descriptive statistics

Standard deviation0.3352875301
Coefficient of variation (CV)1.883740228
Kurtosis2.013685114
Mean0.1779903221
Median Absolute Deviation (MAD)0.03120564833
Skewness1.953256994
Sum23134.82609
Variance0.1124177278
MonotonicityNot monotonic
2022-11-08T22:21:34.889946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117860
 
13.7%
0.00099900099912527
 
9.6%
0.00049975012497706
 
5.9%
0.00033322225923880
 
3.0%
0.00024993751563069
 
2.4%
0.0001999600082955
 
2.3%
0.00016663889351895
 
1.5%
0.042624042621683
 
1.3%
0.00014283673761484
 
1.1%
0.0213226721075
 
0.8%
Other values (18989)75844
58.4%
ValueCountFrequency (%)
1.086944707 × 10-51
 
< 0.1%
1.098889023 × 10-53
< 0.1%
1.298684433 × 10-53
< 0.1%
1.333315556 × 10-51
 
< 0.1%
1.369844249 × 10-54
< 0.1%
1.408430867 × 10-54
< 0.1%
1.428551021 × 10-54
< 0.1%
1.470566609 × 10-53
< 0.1%
1.562475586 × 10-56
< 0.1%
1.587276392 × 10-54
< 0.1%
ValueCountFrequency (%)
117860
13.7%
0.92957845663
 
< 0.1%
0.92899161041
 
< 0.1%
0.87500446411
 
< 0.1%
0.8421135733
 
< 0.1%
0.81722465921
 
< 0.1%
0.81333582221
 
< 0.1%
0.81166917771
 
< 0.1%
0.7977804741
 
< 0.1%
0.79611382961
 
< 0.1%

packet_loss_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24879
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05538746631
Minimum0
Maximum0.5054411049
Zeros74872
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size1015.6 KiB
2022-11-08T22:21:34.965460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1019398953
95-th percentile0.234539068
Maximum0.5054411049
Range0.5054411049
Interquartile range (IQR)0.1019398953

Descriptive statistics

Standard deviation0.0871489775
Coefficient of variation (CV)1.573442212
Kurtosis1.169749735
Mean0.05538746631
Median Absolute Deviation (MAD)0
Skewness1.479209532
Sum7199.152096
Variance0.007594944279
MonotonicityNot monotonic
2022-11-08T22:21:35.043457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074872
57.6%
0.2039202251333
 
1.0%
0.10201106711075
 
0.8%
0.06801870884691
 
0.5%
0.2040221342577
 
0.4%
0.05101828174477
 
0.4%
0.04081666571457
 
0.4%
0.2820478035420
 
0.3%
0.1410943785355
 
0.3%
0.203920225350
 
0.3%
Other values (24869)49371
38.0%
ValueCountFrequency (%)
074872
57.6%
0.0019993690973
 
< 0.1%
0.0020937277176
 
< 0.1%
0.002793427741
 
< 0.1%
0.0027961828641
 
< 0.1%
0.0028553247413
 
< 0.1%
0.0028749474691
 
< 0.1%
0.0029445180271
 
< 0.1%
0.0029582780722
 
< 0.1%
0.0029769375911
 
< 0.1%
ValueCountFrequency (%)
0.50544110491
< 0.1%
0.50527521761
< 0.1%
0.50131311621
< 0.1%
0.50082004981
< 0.1%
0.49404131372
< 0.1%
0.49386974651
< 0.1%
0.49345024511
< 0.1%
0.48980609261
< 0.1%
0.48256332821
< 0.1%
0.481062351
< 0.1%

y
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1015.6 KiB
2.0
64950 
0.0
36695 
1.0
28333 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters389934
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.064950
50.0%
0.036695
28.2%
1.028333
21.8%

Length

2022-11-08T22:21:35.110818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T22:21:35.168448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.064950
50.0%
0.036695
28.2%
1.028333
21.8%

Most occurring characters

ValueCountFrequency (%)
0166673
42.7%
.129978
33.3%
264950
 
16.7%
128333
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number259956
66.7%
Other Punctuation129978
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0166673
64.1%
264950
 
25.0%
128333
 
10.9%
Other Punctuation
ValueCountFrequency (%)
.129978
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common389934
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0166673
42.7%
.129978
33.3%
264950
 
16.7%
128333
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII389934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0166673
42.7%
.129978
33.3%
264950
 
16.7%
128333
 
7.3%

Interactions

2022-11-08T22:21:31.814539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:22.593677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.730856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.717283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.606501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.442395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.278504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.433375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.246291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.110805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.960482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.893830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:22.691203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.811335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.796343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.683082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.518089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.354565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.506102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.322039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.192224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.035714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.969679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:22.773854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.890906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.876654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.758650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.592256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.432108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.579716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.397120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.274863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.109879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.045706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:22.851676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.967605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.954314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.832980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.667189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.531408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.653119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.473395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.354275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.185801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.122138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:22.930433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.057608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.030408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.907014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.741033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.607181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.727726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.549254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.433502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.269665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.197807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.007899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.159028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.113345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.983305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.815184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.687983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.802690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.623810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.510141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.348830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.273966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.093827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.238428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.224950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.060544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.889891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.764875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.873531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.698445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.584114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.429352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.392490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.438567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.314671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.303786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.137972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.966614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.840481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.951124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.781530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.657288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.511095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.472526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.510810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.427060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.380929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.213883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.045772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.917034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.025638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.886009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.735622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.587235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.575326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.581240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.522016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.454687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.289848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.130267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.991787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.098253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.959088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.811956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.662184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:32.660904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:23.655549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:24.609869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:25.531091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:26.368143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:27.204876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:28.361330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:29.170807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.033710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:30.886138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T22:21:31.737532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T22:21:35.219684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T22:21:35.313632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T22:21:35.407875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T22:21:35.501641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T22:21:33.036829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T22:21:33.202667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_stdpacket_loss_ratepacket_loss_stdy
00.1707520.0287680.7282260.0295780.0019530.00.0019530.2866470.1888340.0060940.0291561.0
10.1870120.0223610.7641430.0636520.0019530.00.0019530.3777300.2843690.0028690.0133541.0
20.1992070.0199140.7736790.0490100.0019530.00.0019530.5024520.0999080.0418040.1072141.0
30.1951420.0000000.7812550.0539530.0019530.00.0019530.5422000.0887150.0358520.0965101.0
40.1758330.0337180.7698920.0451130.0019530.00.0019530.5306200.0991410.0537070.1565411.0
50.1626220.0302300.7736790.0393520.0019530.00.0019530.5311720.0738660.0060940.0291561.0
60.1910770.0310730.7518330.0592930.0019530.00.0019530.4954420.0878910.0445290.1544501.0
70.1929240.0104000.7582700.0325550.0019530.00.0019530.5198150.0618450.0001430.0000001.0
80.1971740.0159470.7556870.0270160.0019530.00.0019530.5296200.0439270.0001430.0000001.0
90.1870120.0349880.7519000.0211040.0019530.00.0019530.5340940.0773950.0299000.1189791.0

Last rows

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_stdpacket_loss_ratepacket_loss_stdy
1299680.5333640.0520090.4082100.0608540.0000670.0000000.0000670.0331320.0041940.0744710.2378000.0
1299690.5666960.0963020.4203260.0543550.0000670.0000000.0000670.0683350.1632120.1307840.2645180.0
1299700.5232420.1329690.4861870.1388710.0027860.0092120.0326940.0923290.1986460.0209030.0773260.0
1299710.6194700.0911040.3809770.0707220.0000670.0000000.0000670.0376560.0049680.0028170.0134510.0
1299720.5639180.0962600.3861650.1015030.0000670.0000000.0000670.0741790.1972180.0328070.1331710.0
1299730.5421910.0566520.4088870.1435030.0014260.0066600.0326940.0729660.1826340.0147210.0592760.0
1299740.5116380.1158650.3933250.1635860.0000670.0000000.0000670.0335830.0027950.0000710.0000000.0
1299750.5389200.1746290.4442260.1598590.0000670.0000000.0000670.0563640.0447110.0522570.1738670.0
1299760.5699670.0552440.3903810.0749100.0000670.0000000.0000670.0728420.1853600.0804230.2538890.0
1299770.5500300.0564620.3827820.0592680.0000670.0000000.0000670.0346370.0029450.0387590.1895300.0

Duplicate rows

Most frequently occurring

fps_meanfps_stdrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxbitrate_meanbitrate_stdpacket_loss_ratepacket_loss_stdy# duplicates
710.4000400.00.1081320.01.0000000.01.0000000.5910400.00.0401670.02.059
1170.5001250.00.3125090.01.0000000.01.0000000.5756280.01.0000000.01.056
1180.5001250.00.6667780.00.1168030.00.1168030.4788670.00.1848140.01.056
1200.5002500.00.0003330.01.0000000.01.0000000.5118460.00.0003330.02.054
2241.0000000.00.0003330.01.0000000.01.0000000.9728210.00.4022710.02.054
1080.5000420.00.2273080.01.0000000.01.0000000.4998190.00.1111110.02.048
1380.5556050.00.6444520.01.0000000.01.0000000.5932550.00.2007310.01.045
1130.5001250.00.0729260.00.0409540.00.0409540.5490760.00.0389350.02.034
1730.6667040.00.0272180.00.0213410.00.0213410.5780160.00.0009990.02.033
1840.7272980.00.5827830.00.0001670.00.0001670.0288130.01.0000000.00.033